For all the following experiments, we set \( n_f = {48} \) , resulting in \( n_p = {12179905} \)  and set the ReLU leak coefficient to 0.05.
We trained the regularizer on the Low Dose CT Image and Projection dataset [1]}, where the images were downsampled to \( {128} \times {128} \) .
We optimized (REF ) using Adam [2]} with a learning rate of 5e-4 and set the first and second order momentum variables to \( \beta _1 = {0.9} \)  and \( \beta _2 = {0.999} \) .
To stabilize training, we convolved \( p_x \)  with \( \mathcal {N}(0, \sigma _{\text{data}}^2 \mathrm {Id}_n) \) , where \( \sigma _{\text{data}} = {1.5e-2} \) .
We used a batch size of 25 and a replay buffer holding 8000 images with reinitialization chance of \( p_{\text{re}} = {1}{} \) .
Samples in the buffer were reinitialized with an equal chance of uniform noise or samples from the data distribution.
To sample \( p_\phi \) , we ran (REF ) with \( K = {500} \) , using \( \epsilon = 1 \)  and \( \beta = {7.5e-3} \) .Similar to [3]}, we reparametrize the regularizer as \( \frac{R}{T} \)  for a-priori chosen \( T \) , such that \( \epsilon = 1 \)  in (REF ).
We summarize the training algorithm in Alg. REF .
